Various techniques exist for locating user equipment (UE), such as cell phones, in a wireless network. Early versions of locating UEs included cell tower triangulation, a fairly inaccurate location solution. Since then, many UEs have been configured with global positioning system (GPS) modules that quickly and accurately identify the location of the UE via GPS satellites. However, GPS location solutions are often ineffective indoors as the satellite signals are faint and unable to propagate through certain structures.
Signal strength “fingerprint” techniques have been considered as a solution for indoor localization. In these fingerprint-based techniques, a set of different “training locations” is employed with each location being associated with a parameter of the radio frequency (RF) signal received by one or more indoor wireless access points. Received signal strength values from the wireless access points are concatenated to form a “fingerprint vector” in which the location of the UE is then estimated using various algorithms. However, this solution is also fairly inaccurate in that the amount of data used in the estimation relies on a single type of variable data (i.e., the received signal strength), much like the earlier location techniques of cell tower triangulation.